没有 bazel 的 Tensorflow inception
Tensorflow inception without bazel
我尝试了来自 tensorflow 网站的这个图像识别教程:
https://www.tensorflow.org/tutorials/image_retraining
它成功地与 bazel bu 命令行一起工作
是否可以使用 bazel 或 python 脚本以编程方式调用此初始模型,以便我可以轻松地为其提供图像
您可以使用 tmp 目录下生成的文件并编写 python 脚本来加载模型并生成预测。
此外,建议将文件保存在 tmp 文件夹以外的目录中,因为文件夹中的内容可能会被冲走。
import tensorflow as tf
import sys
image_path = sys.argv[1]
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
#loads label file, strips off carriage return
label_lines = [line.strip() for line in tf.gfile.GFile("/tmp/output_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("/tmp/output_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
# Feed the image data as input to the graph an get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0':image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.2f)' % (human_string, score))
我尝试了来自 tensorflow 网站的这个图像识别教程: https://www.tensorflow.org/tutorials/image_retraining 它成功地与 bazel bu 命令行一起工作 是否可以使用 bazel 或 python 脚本以编程方式调用此初始模型,以便我可以轻松地为其提供图像
您可以使用 tmp 目录下生成的文件并编写 python 脚本来加载模型并生成预测。
此外,建议将文件保存在 tmp 文件夹以外的目录中,因为文件夹中的内容可能会被冲走。
import tensorflow as tf
import sys
image_path = sys.argv[1]
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
#loads label file, strips off carriage return
label_lines = [line.strip() for line in tf.gfile.GFile("/tmp/output_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("/tmp/output_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
# Feed the image data as input to the graph an get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0':image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.2f)' % (human_string, score))